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# Senior GenAI Architect

- **Company:** Whiztek Corp
- **Location:** Schaumburg, IL, United States
- **Experience:** Expert
- **Contract:** Contract
- **Skills:** Artificial Intelligence, Applications Architecture, Software Applications, DevOps, Machine Learning, Datadog, Data Logging, Large Language Models, Grafana, Model Validation, Generative AI, Langfuse — LLM Observability and Analytics Platform, LangSmith, Low Latency, Performance Monitor, Arize Phoenix, Machine Learning Operations, Evaluation of Large Language Models, Dynatrace
- **Published:** November 3, 2026
- **Apply:** https://www.dice.com/job-detail/e67ba108-6a1f-43c5-8b08-d0744333ca23

## About the Role

* Proven experience as an AI Architect, AI/ML Engineer, or similar technical leadership role.
 * Strong knowledge of AI/ML architecture and AI observability practices.
 * Hands-on experience monitoring and troubleshooting AI/ML or Generative AI applications.
 * Understanding of LLM observability, distributed tracing, application logging, metrics, and performance monitoring.
 * Experience with observability tools and frameworks such as LangSmith, Langfuse, Arize Phoenix, OpenTelemetry, Datadog, or similar platforms.
 * Familiarity with LLM evaluation, prompt tracing, model performance, latency, token consumption, and error analysis.
 * Strong analytical, problem-solving, and communication skills.

## Description

We are looking for an experienced AI Architect with expertise in AI Observability to support a short-term engagement focused on evaluating, designing, and improving observability for AI/ML applications and systems. The ideal candidate will have a strong understanding of AI architecture, monitoring, tracing, performance evaluation, and troubleshooting of AI-powered applications, including LLM-based solutions., * Assess existing AI/ML application architecture and observability capabilities.

 * Design and recommend observability strategies for AI/ML and Generative AI applications.
 * Implement or enhance monitoring, logging, tracing, and performance measurement for AI workloads.
 * Establish visibility into LLM interactions, model behavior, latency, token usage, errors, and resource consumption.
 * Define metrics and dashboards to monitor AI application reliability, performance, and operational health.
 * Evaluate AI observability tools and recommend suitable solutions based on project requirements.
 * Identify bottlenecks, troubleshoot issues, and recommend improvements to AI application performance and reliability.
 * Collaborate with engineering, DevOps, and platform teams to establish observability best practices.